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Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT

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Title: Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT
Authors: Tom Finck, David Schinz, Lioba Grundl, Rami Eisawy, Mehmet Yiğitsoy, Julia Moosbauer, Claus Zimmer, Franz Pfister, Benedikt Wiestler
Source: Clin Neuroradiol
Publisher Information: Springer Science and Business Media LLC, 2021.
Publication Year: 2021
Subject Terms: Stroke, 03 medical and health sciences, 0302 clinical medicine, Humans, Original Article, Triage, Machine learning, Artificial intelligence, Emergency imaging, Computed tomography, Humans [MeSH], Triage [MeSH], Ischemic Stroke/diagnostic imaging [MeSH], Stroke/diagnostic imaging [MeSH], Retrospective Studies [MeSH], Tomography, X-Ray Computed/methods [MeSH], Tomography, X-Ray Computed, ddc, Ischemic Stroke, Retrospective Studies, 3. Good health
Description: Purpose Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage. Methods Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed. Anomaly detection was performed using a weakly supervised ML classifier. Findings were displayed on a voxel-level (heatmap) and pooled to an anomaly score. Thresholds for this score classified patients into i) normal, ii) inconclusive, iii) pathological. Expert-validated radiological reports were considered as ground truth. Test assessment was performed with ROC analysis; inconclusive results were pooled to pathological predictions for accuracy measurements. Results During the investigation period 208 patients were referred for head CT of which 111 could be included. Definite ratings into normal/pathological were feasible in 77 (69.4%) patients. Based on anomaly scores, the AUC to differentiate normal from pathological scans was 0.98 (95% CI 0.97–1.00). The sensitivity, specificity, positive and negative predictive values were 100%, 40.6%, 80.6% and 100%, respectively. Conclusion Our study demonstrates the potential of a weakly supervised anomaly-detection tool to detect stroke findings in head CT. Definite classification into normal/pathological was made with high accuracy in > 2/3 of patients. Anomaly heatmaps further provide guidance towards pathologies, also in cases with inconclusive ratings.
Document Type: Article
Other literature type
File Description: application/pdf
Language: English
ISSN: 1869-1447
1869-1439
DOI: 10.1007/s00062-021-01081-7
Access URL: https://link.springer.com/content/pdf/10.1007/s00062-021-01081-7.pdf
https://pubmed.ncbi.nlm.nih.gov/34463778
https://link.springer.com/content/pdf/10.1007/s00062-021-01081-7.pdf
https://link.springer.com/article/10.1007/s00062-021-01081-7
https://repository.publisso.de/resource/frl:6452162
https://mediatum.ub.tum.de/doc/1705261/document.pdf
Rights: CC BY
Accession Number: edsair.doi.dedup.....6cc9d60d19eb117cedc6cc0237f231f8
Database: OpenAIRE
Description
Abstract:Purpose Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage. Methods Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed. Anomaly detection was performed using a weakly supervised ML classifier. Findings were displayed on a voxel-level (heatmap) and pooled to an anomaly score. Thresholds for this score classified patients into i) normal, ii) inconclusive, iii) pathological. Expert-validated radiological reports were considered as ground truth. Test assessment was performed with ROC analysis; inconclusive results were pooled to pathological predictions for accuracy measurements. Results During the investigation period 208 patients were referred for head CT of which 111 could be included. Definite ratings into normal/pathological were feasible in 77 (69.4%) patients. Based on anomaly scores, the AUC to differentiate normal from pathological scans was 0.98 (95% CI 0.97–1.00). The sensitivity, specificity, positive and negative predictive values were 100%, 40.6%, 80.6% and 100%, respectively. Conclusion Our study demonstrates the potential of a weakly supervised anomaly-detection tool to detect stroke findings in head CT. Definite classification into normal/pathological was made with high accuracy in > 2/3 of patients. Anomaly heatmaps further provide guidance towards pathologies, also in cases with inconclusive ratings.
ISSN:18691447
18691439
DOI:10.1007/s00062-021-01081-7